Cable fault and assessment using multiple frequency autoencoder regression-based reflectometry

Abstract

The distribution cable may be considered the most critical element for power system operation through the key functions of electricity supplement and control and instrumentation signal transmission. Hence, there is a growing need for cable diagnostic techniques that enable accurate condition monitoring and fault detection in cables where external artifact signals are to be continuously measured. This research presents a technique for detecting cable faults based on autoencoder regression-based reflectometry with multiple frequency sinusoidal signals. Estimations of the reflected signal and preliminary test results of a non-faulty cable are used to train the time-series signal reconstruction and anomaly detection, allowing the distinguishment between a fault-induced reflected signal and various artifacts resulting from noise, input mismatch, or other factors. Experiment results on fault location in bypass cable and the reflected signal discrimination on branched network cable have validated the usefulness of the proposed algorithm.

Optimized Fabrication Process and PD Characteristics of MVDC Multilayer Insulation Cable Systems for Next Generation Wide-Body All-Electric Aircraft

“…By developing portable PD-detection systems and synthetic calibrators, these studies address the critical need for early fault detection and improved measurement accuracy in high-voltage environments. Concurrently, advancements in cable fault detection, such as autoencoder regression-based reflectometry [26], and the utilization of damped AC testing in offshore wind farms [27], complement these efforts by offering efficient methods for identifying faults and ensuring the integrity of electrical infrastructure. In [28], the authors further contribute by providing a test platform to simulate real-world conditions for PD-measuring systems, thereby enhancing their reliability and practicality.…”